Following pathway analysis, appropriate data reporting and interpretation are imperative. Currently, bias introduced by gene size is less commonly addressed than bias from pathway size. In particular, large genes containing many SNPs are more likely to contain significant SNPs by chance alone [63]; for analyses, this can favor pathways containing large genes. Analytical tools that employ permutations naturally control for gene size by comparing the actual association data to the distribution of association statistics generated from randomly permuted data sets expected to reflect chance-based confounding effects. Other approaches [41, 42] allow users to restrict analysis to a subset of the most significant SNPs in each gene: for large genes, this may eliminate some spuriously-associated SNPs and thus limit their impact on the pathway analysis. At minimum, studies should discuss potential impacts of gene and pathway size on their results. Other sources of bias that should be addressed include the capacity for strongly-associated markers to drive pathway association and the possible effects of SNPs being assigned to multiple genes.
